Load scripts: loads libraries and useful scripts used in the analyses; all .R files contained in scripts at the root of the factory are automatically loaded
Load data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)These scripts will load:
.R files inside /scripts/.R files inside /src/These scripts also contain routines to access the latest clean encrypted data (see next section).
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 358,060 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… miss… e380000… nhs_glo… 1 gl34fe South West
## [90m 2[39m 111 2020-03-18 fema… miss… e380001… nhs_sou… 1 ne325nn North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_air… 8 bd57jr North Eas…
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ash… 7 tn254ab South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 9 n111np London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 11 s752py North Eas…
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 19 ss143hg East of E…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 6 dn227xf North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bat… 9 ba25rp South West
## [90m# … with 358,050 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 65
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 100
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 14
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 7
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 1
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 8
## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 4
## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 4
## 113 2020-06-21 East of England 3
## 114 2020-06-22 East of England 6
## 115 2020-06-23 East of England 5
## 116 2020-06-24 East of England 4
## 117 2020-06-25 East of England 1
## 118 2020-06-26 East of England 5
## 119 2020-06-27 East of England 6
## 120 2020-06-28 East of England 8
## 121 2020-06-29 East of England 4
## 122 2020-06-30 East of England 5
## 123 2020-07-01 East of England 2
## 124 2020-07-02 East of England 5
## 125 2020-07-03 East of England 0
## 126 2020-07-04 East of England 3
## 127 2020-07-05 East of England 1
## 128 2020-07-06 East of England 2
## 129 2020-07-07 East of England 2
## 130 2020-07-08 East of England 0
## 131 2020-07-09 East of England 8
## 132 2020-07-10 East of England 4
## 133 2020-07-11 East of England 2
## 134 2020-07-12 East of England 1
## 135 2020-07-13 East of England 8
## 136 2020-07-14 East of England 2
## 137 2020-07-15 East of England 0
## 138 2020-07-16 East of England 0
## 139 2020-07-17 East of England 0
## 140 2020-07-18 East of England 0
## 141 2020-07-19 East of England 1
## 142 2020-07-20 East of England 1
## 143 2020-07-21 East of England 1
## 144 2020-07-22 East of England 2
## 145 2020-07-23 East of England 1
## 146 2020-07-24 East of England 1
## 147 2020-07-25 East of England 0
## 148 2020-07-26 East of England 1
## 149 2020-07-27 East of England 1
## 150 2020-07-28 East of England 2
## 151 2020-07-29 East of England 0
## 152 2020-07-30 East of England 0
## 153 2020-07-31 East of England 1
## 154 2020-08-01 East of England 0
## 155 2020-08-02 East of England 0
## 156 2020-08-03 East of England 0
## 157 2020-08-04 East of England 1
## 158 2020-08-05 East of England 1
## 159 2020-08-06 East of England 0
## 160 2020-08-07 East of England 1
## 161 2020-08-08 East of England 0
## 162 2020-08-09 East of England 0
## 163 2020-08-10 East of England 1
## 164 2020-08-11 East of England 2
## 165 2020-08-12 East of England 1
## 166 2020-08-13 East of England 0
## 167 2020-08-14 East of England 1
## 168 2020-08-15 East of England 1
## 169 2020-08-16 East of England 0
## 170 2020-08-17 East of England 0
## 171 2020-08-18 East of England 2
## 172 2020-08-19 East of England 1
## 173 2020-08-20 East of England 1
## 174 2020-08-21 East of England 0
## 175 2020-08-22 East of England 1
## 176 2020-08-23 East of England 1
## 177 2020-08-24 East of England 0
## 178 2020-08-25 East of England 0
## 179 2020-08-26 East of England 1
## 180 2020-08-27 East of England 1
## 181 2020-08-28 East of England 0
## 182 2020-08-29 East of England 0
## 183 2020-08-30 East of England 0
## 184 2020-08-31 East of England 0
## 185 2020-09-01 East of England 0
## 186 2020-09-02 East of England 0
## 187 2020-09-03 East of England 1
## 188 2020-09-04 East of England 1
## 189 2020-09-05 East of England 0
## 190 2020-09-06 East of England 1
## 191 2020-09-07 East of England 0
## 192 2020-09-08 East of England 0
## 193 2020-09-09 East of England 0
## 194 2020-09-10 East of England 0
## 195 2020-09-11 East of England 0
## 196 2020-09-12 East of England 0
## 197 2020-09-13 East of England 1
## 198 2020-09-14 East of England 1
## 199 2020-09-15 East of England 0
## 200 2020-09-16 East of England 0
## 201 2020-09-17 East of England 0
## 202 2020-09-18 East of England 0
## 203 2020-09-19 East of England 0
## 204 2020-09-20 East of England 2
## 205 2020-09-21 East of England 0
## 206 2020-09-22 East of England 2
## 207 2020-09-23 East of England 1
## 208 2020-09-24 East of England 0
## 209 2020-09-25 East of England 1
## 210 2020-09-26 East of England 1
## 211 2020-09-27 East of England 1
## 212 2020-09-28 East of England 2
## 213 2020-09-29 East of England 2
## 214 2020-09-30 East of England 2
## 215 2020-10-01 East of England 2
## 216 2020-10-02 East of England 1
## 217 2020-10-03 East of England 1
## 218 2020-10-04 East of England 0
## 219 2020-10-05 East of England 0
## 220 2020-10-06 East of England 4
## 221 2020-10-07 East of England 6
## 222 2020-10-08 East of England 3
## 223 2020-10-09 East of England 1
## 224 2020-10-10 East of England 6
## 225 2020-10-11 East of England 2
## 226 2020-10-12 East of England 2
## 227 2020-10-13 East of England 1
## 228 2020-10-14 East of England 3
## 229 2020-10-15 East of England 4
## 230 2020-10-16 East of England 5
## 231 2020-10-17 East of England 6
## 232 2020-10-18 East of England 7
## 233 2020-10-19 East of England 5
## 234 2020-10-20 East of England 9
## 235 2020-10-21 East of England 7
## 236 2020-10-22 East of England 7
## 237 2020-10-23 East of England 14
## 238 2020-10-24 East of England 1
## 239 2020-10-25 East of England 10
## 240 2020-10-26 East of England 10
## 241 2020-10-27 East of England 6
## 242 2020-10-28 East of England 12
## 243 2020-10-29 East of England 10
## 244 2020-10-30 East of England 12
## 245 2020-10-31 East of England 15
## 246 2020-11-01 East of England 14
## 247 2020-11-02 East of England 8
## 248 2020-11-03 East of England 14
## 249 2020-11-04 East of England 10
## 250 2020-11-05 East of England 10
## 251 2020-11-06 East of England 17
## 252 2020-11-07 East of England 10
## 253 2020-11-08 East of England 13
## 254 2020-11-09 East of England 13
## 255 2020-11-10 East of England 26
## 256 2020-11-11 East of England 14
## 257 2020-11-12 East of England 14
## 258 2020-11-13 East of England 21
## 259 2020-11-14 East of England 19
## 260 2020-11-15 East of England 13
## 261 2020-11-16 East of England 11
## 262 2020-11-17 East of England 17
## 263 2020-11-18 East of England 19
## 264 2020-11-19 East of England 23
## 265 2020-11-20 East of England 23
## 266 2020-11-21 East of England 18
## 267 2020-11-22 East of England 20
## 268 2020-11-23 East of England 18
## 269 2020-11-24 East of England 20
## 270 2020-11-25 East of England 19
## 271 2020-11-26 East of England 19
## 272 2020-11-27 East of England 14
## 273 2020-11-28 East of England 27
## 274 2020-11-29 East of England 19
## 275 2020-11-30 East of England 21
## 276 2020-12-01 East of England 24
## 277 2020-12-02 East of England 16
## 278 2020-12-03 East of England 23
## 279 2020-12-04 East of England 22
## 280 2020-12-05 East of England 20
## 281 2020-12-06 East of England 17
## 282 2020-12-07 East of England 12
## 283 2020-12-08 East of England 21
## 284 2020-12-09 East of England 2
## 285 2020-03-01 London 0
## 286 2020-03-02 London 0
## 287 2020-03-03 London 0
## 288 2020-03-04 London 0
## 289 2020-03-05 London 0
## 290 2020-03-06 London 1
## 291 2020-03-07 London 0
## 292 2020-03-08 London 0
## 293 2020-03-09 London 1
## 294 2020-03-10 London 0
## 295 2020-03-11 London 5
## 296 2020-03-12 London 6
## 297 2020-03-13 London 10
## 298 2020-03-14 London 13
## 299 2020-03-15 London 9
## 300 2020-03-16 London 15
## 301 2020-03-17 London 23
## 302 2020-03-18 London 28
## 303 2020-03-19 London 25
## 304 2020-03-20 London 44
## 305 2020-03-21 London 49
## 306 2020-03-22 London 54
## 307 2020-03-23 London 63
## 308 2020-03-24 London 86
## 309 2020-03-25 London 112
## 310 2020-03-26 London 130
## 311 2020-03-27 London 130
## 312 2020-03-28 London 123
## 313 2020-03-29 London 145
## 314 2020-03-30 London 151
## 315 2020-03-31 London 183
## 316 2020-04-01 London 202
## 317 2020-04-02 London 191
## 318 2020-04-03 London 199
## 319 2020-04-04 London 231
## 320 2020-04-05 London 195
## 321 2020-04-06 London 198
## 322 2020-04-07 London 220
## 323 2020-04-08 London 239
## 324 2020-04-09 London 207
## 325 2020-04-10 London 171
## 326 2020-04-11 London 178
## 327 2020-04-12 London 159
## 328 2020-04-13 London 166
## 329 2020-04-14 London 143
## 330 2020-04-15 London 143
## 331 2020-04-16 London 140
## 332 2020-04-17 London 101
## 333 2020-04-18 London 101
## 334 2020-04-19 London 103
## 335 2020-04-20 London 96
## 336 2020-04-21 London 96
## 337 2020-04-22 London 109
## 338 2020-04-23 London 77
## 339 2020-04-24 London 71
## 340 2020-04-25 London 58
## 341 2020-04-26 London 53
## 342 2020-04-27 London 52
## 343 2020-04-28 London 44
## 344 2020-04-29 London 45
## 345 2020-04-30 London 40
## 346 2020-05-01 London 41
## 347 2020-05-02 London 41
## 348 2020-05-03 London 36
## 349 2020-05-04 London 30
## 350 2020-05-05 London 25
## 351 2020-05-06 London 37
## 352 2020-05-07 London 37
## 353 2020-05-08 London 30
## 354 2020-05-09 London 23
## 355 2020-05-10 London 26
## 356 2020-05-11 London 18
## 357 2020-05-12 London 18
## 358 2020-05-13 London 17
## 359 2020-05-14 London 20
## 360 2020-05-15 London 19
## 361 2020-05-16 London 14
## 362 2020-05-17 London 15
## 363 2020-05-18 London 11
## 364 2020-05-19 London 14
## 365 2020-05-20 London 19
## 366 2020-05-21 London 12
## 367 2020-05-22 London 10
## 368 2020-05-23 London 6
## 369 2020-05-24 London 7
## 370 2020-05-25 London 9
## 371 2020-05-26 London 14
## 372 2020-05-27 London 7
## 373 2020-05-28 London 8
## 374 2020-05-29 London 7
## 375 2020-05-30 London 12
## 376 2020-05-31 London 6
## 377 2020-06-01 London 10
## 378 2020-06-02 London 8
## 379 2020-06-03 London 6
## 380 2020-06-04 London 8
## 381 2020-06-05 London 4
## 382 2020-06-06 London 0
## 383 2020-06-07 London 5
## 384 2020-06-08 London 5
## 385 2020-06-09 London 5
## 386 2020-06-10 London 8
## 387 2020-06-11 London 5
## 388 2020-06-12 London 3
## 389 2020-06-13 London 3
## 390 2020-06-14 London 3
## 391 2020-06-15 London 1
## 392 2020-06-16 London 2
## 393 2020-06-17 London 1
## 394 2020-06-18 London 2
## 395 2020-06-19 London 5
## 396 2020-06-20 London 3
## 397 2020-06-21 London 4
## 398 2020-06-22 London 2
## 399 2020-06-23 London 1
## 400 2020-06-24 London 4
## 401 2020-06-25 London 3
## 402 2020-06-26 London 2
## 403 2020-06-27 London 1
## 404 2020-06-28 London 2
## 405 2020-06-29 London 2
## 406 2020-06-30 London 1
## 407 2020-07-01 London 3
## 408 2020-07-02 London 2
## 409 2020-07-03 London 2
## 410 2020-07-04 London 1
## 411 2020-07-05 London 3
## 412 2020-07-06 London 2
## 413 2020-07-07 London 1
## 414 2020-07-08 London 3
## 415 2020-07-09 London 4
## 416 2020-07-10 London 0
## 417 2020-07-11 London 1
## 418 2020-07-12 London 1
## 419 2020-07-13 London 1
## 420 2020-07-14 London 0
## 421 2020-07-15 London 2
## 422 2020-07-16 London 0
## 423 2020-07-17 London 0
## 424 2020-07-18 London 2
## 425 2020-07-19 London 0
## 426 2020-07-20 London 0
## 427 2020-07-21 London 1
## 428 2020-07-22 London 0
## 429 2020-07-23 London 2
## 430 2020-07-24 London 0
## 431 2020-07-25 London 1
## 432 2020-07-26 London 0
## 433 2020-07-27 London 1
## 434 2020-07-28 London 0
## 435 2020-07-29 London 0
## 436 2020-07-30 London 1
## 437 2020-07-31 London 0
## 438 2020-08-01 London 0
## 439 2020-08-02 London 3
## 440 2020-08-03 London 0
## 441 2020-08-04 London 0
## 442 2020-08-05 London 0
## 443 2020-08-06 London 1
## 444 2020-08-07 London 0
## 445 2020-08-08 London 0
## 446 2020-08-09 London 0
## 447 2020-08-10 London 0
## 448 2020-08-11 London 1
## 449 2020-08-12 London 0
## 450 2020-08-13 London 2
## 451 2020-08-14 London 0
## 452 2020-08-15 London 0
## 453 2020-08-16 London 0
## 454 2020-08-17 London 1
## 455 2020-08-18 London 1
## 456 2020-08-19 London 0
## 457 2020-08-20 London 1
## 458 2020-08-21 London 0
## 459 2020-08-22 London 0
## 460 2020-08-23 London 0
## 461 2020-08-24 London 1
## 462 2020-08-25 London 1
## 463 2020-08-26 London 0
## 464 2020-08-27 London 0
## 465 2020-08-28 London 0
## 466 2020-08-29 London 0
## 467 2020-08-30 London 0
## 468 2020-08-31 London 1
## 469 2020-09-01 London 0
## 470 2020-09-02 London 1
## 471 2020-09-03 London 1
## 472 2020-09-04 London 0
## 473 2020-09-05 London 0
## 474 2020-09-06 London 2
## 475 2020-09-07 London 0
## 476 2020-09-08 London 0
## 477 2020-09-09 London 0
## 478 2020-09-10 London 2
## 479 2020-09-11 London 1
## 480 2020-09-12 London 1
## 481 2020-09-13 London 0
## 482 2020-09-14 London 0
## 483 2020-09-15 London 1
## 484 2020-09-16 London 2
## 485 2020-09-17 London 2
## 486 2020-09-18 London 1
## 487 2020-09-19 London 3
## 488 2020-09-20 London 3
## 489 2020-09-21 London 2
## 490 2020-09-22 London 6
## 491 2020-09-23 London 4
## 492 2020-09-24 London 3
## 493 2020-09-25 London 1
## 494 2020-09-26 London 1
## 495 2020-09-27 London 1
## 496 2020-09-28 London 3
## 497 2020-09-29 London 7
## 498 2020-09-30 London 6
## 499 2020-10-01 London 4
## 500 2020-10-02 London 1
## 501 2020-10-03 London 3
## 502 2020-10-04 London 2
## 503 2020-10-05 London 7
## 504 2020-10-06 London 4
## 505 2020-10-07 London 6
## 506 2020-10-08 London 6
## 507 2020-10-09 London 7
## 508 2020-10-10 London 3
## 509 2020-10-11 London 5
## 510 2020-10-12 London 7
## 511 2020-10-13 London 4
## 512 2020-10-14 London 6
## 513 2020-10-15 London 13
## 514 2020-10-16 London 6
## 515 2020-10-17 London 2
## 516 2020-10-18 London 5
## 517 2020-10-19 London 11
## 518 2020-10-20 London 8
## 519 2020-10-21 London 14
## 520 2020-10-22 London 12
## 521 2020-10-23 London 7
## 522 2020-10-24 London 18
## 523 2020-10-25 London 10
## 524 2020-10-26 London 10
## 525 2020-10-27 London 12
## 526 2020-10-28 London 23
## 527 2020-10-29 London 14
## 528 2020-10-30 London 17
## 529 2020-10-31 London 7
## 530 2020-11-01 London 17
## 531 2020-11-02 London 16
## 532 2020-11-03 London 10
## 533 2020-11-04 London 18
## 534 2020-11-05 London 17
## 535 2020-11-06 London 12
## 536 2020-11-07 London 21
## 537 2020-11-08 London 14
## 538 2020-11-09 London 28
## 539 2020-11-10 London 14
## 540 2020-11-11 London 14
## 541 2020-11-12 London 15
## 542 2020-11-13 London 14
## 543 2020-11-14 London 20
## 544 2020-11-15 London 18
## 545 2020-11-16 London 28
## 546 2020-11-17 London 29
## 547 2020-11-18 London 21
## 548 2020-11-19 London 23
## 549 2020-11-20 London 19
## 550 2020-11-21 London 18
## 551 2020-11-22 London 26
## 552 2020-11-23 London 19
## 553 2020-11-24 London 25
## 554 2020-11-25 London 30
## 555 2020-11-26 London 25
## 556 2020-11-27 London 28
## 557 2020-11-28 London 23
## 558 2020-11-29 London 39
## 559 2020-11-30 London 18
## 560 2020-12-01 London 28
## 561 2020-12-02 London 28
## 562 2020-12-03 London 26
## 563 2020-12-04 London 28
## 564 2020-12-05 London 15
## 565 2020-12-06 London 21
## 566 2020-12-07 London 23
## 567 2020-12-08 London 23
## 568 2020-12-09 London 2
## 569 2020-03-01 Midlands 0
## 570 2020-03-02 Midlands 0
## 571 2020-03-03 Midlands 1
## 572 2020-03-04 Midlands 0
## 573 2020-03-05 Midlands 0
## 574 2020-03-06 Midlands 0
## 575 2020-03-07 Midlands 0
## 576 2020-03-08 Midlands 2
## 577 2020-03-09 Midlands 1
## 578 2020-03-10 Midlands 0
## 579 2020-03-11 Midlands 2
## 580 2020-03-12 Midlands 6
## 581 2020-03-13 Midlands 5
## 582 2020-03-14 Midlands 4
## 583 2020-03-15 Midlands 5
## 584 2020-03-16 Midlands 11
## 585 2020-03-17 Midlands 8
## 586 2020-03-18 Midlands 13
## 587 2020-03-19 Midlands 8
## 588 2020-03-20 Midlands 28
## 589 2020-03-21 Midlands 13
## 590 2020-03-22 Midlands 31
## 591 2020-03-23 Midlands 33
## 592 2020-03-24 Midlands 41
## 593 2020-03-25 Midlands 48
## 594 2020-03-26 Midlands 64
## 595 2020-03-27 Midlands 72
## 596 2020-03-28 Midlands 89
## 597 2020-03-29 Midlands 92
## 598 2020-03-30 Midlands 90
## 599 2020-03-31 Midlands 123
## 600 2020-04-01 Midlands 140
## 601 2020-04-02 Midlands 142
## 602 2020-04-03 Midlands 124
## 603 2020-04-04 Midlands 151
## 604 2020-04-05 Midlands 164
## 605 2020-04-06 Midlands 140
## 606 2020-04-07 Midlands 123
## 607 2020-04-08 Midlands 186
## 608 2020-04-09 Midlands 139
## 609 2020-04-10 Midlands 127
## 610 2020-04-11 Midlands 142
## 611 2020-04-12 Midlands 139
## 612 2020-04-13 Midlands 120
## 613 2020-04-14 Midlands 116
## 614 2020-04-15 Midlands 147
## 615 2020-04-16 Midlands 102
## 616 2020-04-17 Midlands 118
## 617 2020-04-18 Midlands 115
## 618 2020-04-19 Midlands 92
## 619 2020-04-20 Midlands 107
## 620 2020-04-21 Midlands 86
## 621 2020-04-22 Midlands 78
## 622 2020-04-23 Midlands 103
## 623 2020-04-24 Midlands 79
## 624 2020-04-25 Midlands 72
## 625 2020-04-26 Midlands 81
## 626 2020-04-27 Midlands 74
## 627 2020-04-28 Midlands 68
## 628 2020-04-29 Midlands 53
## 629 2020-04-30 Midlands 56
## 630 2020-05-01 Midlands 64
## 631 2020-05-02 Midlands 51
## 632 2020-05-03 Midlands 52
## 633 2020-05-04 Midlands 61
## 634 2020-05-05 Midlands 59
## 635 2020-05-06 Midlands 59
## 636 2020-05-07 Midlands 48
## 637 2020-05-08 Midlands 34
## 638 2020-05-09 Midlands 37
## 639 2020-05-10 Midlands 42
## 640 2020-05-11 Midlands 33
## 641 2020-05-12 Midlands 45
## 642 2020-05-13 Midlands 40
## 643 2020-05-14 Midlands 39
## 644 2020-05-15 Midlands 40
## 645 2020-05-16 Midlands 34
## 646 2020-05-17 Midlands 31
## 647 2020-05-18 Midlands 36
## 648 2020-05-19 Midlands 35
## 649 2020-05-20 Midlands 36
## 650 2020-05-21 Midlands 32
## 651 2020-05-22 Midlands 27
## 652 2020-05-23 Midlands 34
## 653 2020-05-24 Midlands 20
## 654 2020-05-25 Midlands 26
## 655 2020-05-26 Midlands 33
## 656 2020-05-27 Midlands 29
## 657 2020-05-28 Midlands 28
## 658 2020-05-29 Midlands 20
## 659 2020-05-30 Midlands 21
## 660 2020-05-31 Midlands 22
## 661 2020-06-01 Midlands 20
## 662 2020-06-02 Midlands 22
## 663 2020-06-03 Midlands 24
## 664 2020-06-04 Midlands 16
## 665 2020-06-05 Midlands 21
## 666 2020-06-06 Midlands 20
## 667 2020-06-07 Midlands 17
## 668 2020-06-08 Midlands 16
## 669 2020-06-09 Midlands 18
## 670 2020-06-10 Midlands 15
## 671 2020-06-11 Midlands 13
## 672 2020-06-12 Midlands 12
## 673 2020-06-13 Midlands 6
## 674 2020-06-14 Midlands 18
## 675 2020-06-15 Midlands 12
## 676 2020-06-16 Midlands 15
## 677 2020-06-17 Midlands 11
## 678 2020-06-18 Midlands 15
## 679 2020-06-19 Midlands 10
## 680 2020-06-20 Midlands 15
## 681 2020-06-21 Midlands 14
## 682 2020-06-22 Midlands 14
## 683 2020-06-23 Midlands 16
## 684 2020-06-24 Midlands 15
## 685 2020-06-25 Midlands 18
## 686 2020-06-26 Midlands 5
## 687 2020-06-27 Midlands 5
## 688 2020-06-28 Midlands 7
## 689 2020-06-29 Midlands 6
## 690 2020-06-30 Midlands 6
## 691 2020-07-01 Midlands 7
## 692 2020-07-02 Midlands 10
## 693 2020-07-03 Midlands 3
## 694 2020-07-04 Midlands 4
## 695 2020-07-05 Midlands 6
## 696 2020-07-06 Midlands 5
## 697 2020-07-07 Midlands 3
## 698 2020-07-08 Midlands 5
## 699 2020-07-09 Midlands 9
## 700 2020-07-10 Midlands 3
## 701 2020-07-11 Midlands 0
## 702 2020-07-12 Midlands 5
## 703 2020-07-13 Midlands 1
## 704 2020-07-14 Midlands 1
## 705 2020-07-15 Midlands 6
## 706 2020-07-16 Midlands 2
## 707 2020-07-17 Midlands 3
## 708 2020-07-18 Midlands 3
## 709 2020-07-19 Midlands 3
## 710 2020-07-20 Midlands 3
## 711 2020-07-21 Midlands 1
## 712 2020-07-22 Midlands 2
## 713 2020-07-23 Midlands 6
## 714 2020-07-24 Midlands 1
## 715 2020-07-25 Midlands 4
## 716 2020-07-26 Midlands 4
## 717 2020-07-27 Midlands 5
## 718 2020-07-28 Midlands 1
## 719 2020-07-29 Midlands 1
## 720 2020-07-30 Midlands 1
## 721 2020-07-31 Midlands 2
## 722 2020-08-01 Midlands 0
## 723 2020-08-02 Midlands 1
## 724 2020-08-03 Midlands 2
## 725 2020-08-04 Midlands 1
## 726 2020-08-05 Midlands 1
## 727 2020-08-06 Midlands 0
## 728 2020-08-07 Midlands 3
## 729 2020-08-08 Midlands 2
## 730 2020-08-09 Midlands 0
## 731 2020-08-10 Midlands 0
## 732 2020-08-11 Midlands 2
## 733 2020-08-12 Midlands 0
## 734 2020-08-13 Midlands 0
## 735 2020-08-14 Midlands 0
## 736 2020-08-15 Midlands 1
## 737 2020-08-16 Midlands 0
## 738 2020-08-17 Midlands 0
## 739 2020-08-18 Midlands 0
## 740 2020-08-19 Midlands 0
## 741 2020-08-20 Midlands 0
## 742 2020-08-21 Midlands 1
## 743 2020-08-22 Midlands 0
## 744 2020-08-23 Midlands 0
## 745 2020-08-24 Midlands 0
## 746 2020-08-25 Midlands 2
## 747 2020-08-26 Midlands 3
## 748 2020-08-27 Midlands 2
## 749 2020-08-28 Midlands 1
## 750 2020-08-29 Midlands 0
## 751 2020-08-30 Midlands 2
## 752 2020-08-31 Midlands 1
## 753 2020-09-01 Midlands 0
## 754 2020-09-02 Midlands 2
## 755 2020-09-03 Midlands 0
## 756 2020-09-04 Midlands 0
## 757 2020-09-05 Midlands 0
## 758 2020-09-06 Midlands 1
## 759 2020-09-07 Midlands 1
## 760 2020-09-08 Midlands 3
## 761 2020-09-09 Midlands 0
## 762 2020-09-10 Midlands 1
## 763 2020-09-11 Midlands 1
## 764 2020-09-12 Midlands 2
## 765 2020-09-13 Midlands 4
## 766 2020-09-14 Midlands 1
## 767 2020-09-15 Midlands 2
## 768 2020-09-16 Midlands 3
## 769 2020-09-17 Midlands 2
## 770 2020-09-18 Midlands 5
## 771 2020-09-19 Midlands 2
## 772 2020-09-20 Midlands 7
## 773 2020-09-21 Midlands 3
## 774 2020-09-22 Midlands 4
## 775 2020-09-23 Midlands 10
## 776 2020-09-24 Midlands 7
## 777 2020-09-25 Midlands 4
## 778 2020-09-26 Midlands 5
## 779 2020-09-27 Midlands 9
## 780 2020-09-28 Midlands 6
## 781 2020-09-29 Midlands 4
## 782 2020-09-30 Midlands 5
## 783 2020-10-01 Midlands 8
## 784 2020-10-02 Midlands 7
## 785 2020-10-03 Midlands 6
## 786 2020-10-04 Midlands 7
## 787 2020-10-05 Midlands 6
## 788 2020-10-06 Midlands 5
## 789 2020-10-07 Midlands 9
## 790 2020-10-08 Midlands 8
## 791 2020-10-09 Midlands 7
## 792 2020-10-10 Midlands 2
## 793 2020-10-11 Midlands 15
## 794 2020-10-12 Midlands 7
## 795 2020-10-13 Midlands 16
## 796 2020-10-14 Midlands 12
## 797 2020-10-15 Midlands 11
## 798 2020-10-16 Midlands 18
## 799 2020-10-17 Midlands 25
## 800 2020-10-18 Midlands 11
## 801 2020-10-19 Midlands 14
## 802 2020-10-20 Midlands 19
## 803 2020-10-21 Midlands 15
## 804 2020-10-22 Midlands 34
## 805 2020-10-23 Midlands 32
## 806 2020-10-24 Midlands 24
## 807 2020-10-25 Midlands 30
## 808 2020-10-26 Midlands 33
## 809 2020-10-27 Midlands 38
## 810 2020-10-28 Midlands 30
## 811 2020-10-29 Midlands 42
## 812 2020-10-30 Midlands 42
## 813 2020-10-31 Midlands 50
## 814 2020-11-01 Midlands 44
## 815 2020-11-02 Midlands 58
## 816 2020-11-03 Midlands 36
## 817 2020-11-04 Midlands 66
## 818 2020-11-05 Midlands 49
## 819 2020-11-06 Midlands 43
## 820 2020-11-07 Midlands 60
## 821 2020-11-08 Midlands 55
## 822 2020-11-09 Midlands 66
## 823 2020-11-10 Midlands 68
## 824 2020-11-11 Midlands 56
## 825 2020-11-12 Midlands 64
## 826 2020-11-13 Midlands 46
## 827 2020-11-14 Midlands 66
## 828 2020-11-15 Midlands 72
## 829 2020-11-16 Midlands 65
## 830 2020-11-17 Midlands 66
## 831 2020-11-18 Midlands 83
## 832 2020-11-19 Midlands 72
## 833 2020-11-20 Midlands 86
## 834 2020-11-21 Midlands 58
## 835 2020-11-22 Midlands 84
## 836 2020-11-23 Midlands 77
## 837 2020-11-24 Midlands 72
## 838 2020-11-25 Midlands 74
## 839 2020-11-26 Midlands 75
## 840 2020-11-27 Midlands 75
## 841 2020-11-28 Midlands 77
## 842 2020-11-29 Midlands 83
## 843 2020-11-30 Midlands 78
## 844 2020-12-01 Midlands 73
## 845 2020-12-02 Midlands 64
## 846 2020-12-03 Midlands 81
## 847 2020-12-04 Midlands 66
## 848 2020-12-05 Midlands 70
## 849 2020-12-06 Midlands 68
## 850 2020-12-07 Midlands 61
## 851 2020-12-08 Midlands 47
## 852 2020-12-09 Midlands 9
## 853 2020-03-01 North East and Yorkshire 0
## 854 2020-03-02 North East and Yorkshire 0
## 855 2020-03-03 North East and Yorkshire 0
## 856 2020-03-04 North East and Yorkshire 0
## 857 2020-03-05 North East and Yorkshire 0
## 858 2020-03-06 North East and Yorkshire 0
## 859 2020-03-07 North East and Yorkshire 0
## 860 2020-03-08 North East and Yorkshire 0
## 861 2020-03-09 North East and Yorkshire 0
## 862 2020-03-10 North East and Yorkshire 0
## 863 2020-03-11 North East and Yorkshire 0
## 864 2020-03-12 North East and Yorkshire 0
## 865 2020-03-13 North East and Yorkshire 0
## 866 2020-03-14 North East and Yorkshire 0
## 867 2020-03-15 North East and Yorkshire 2
## 868 2020-03-16 North East and Yorkshire 3
## 869 2020-03-17 North East and Yorkshire 1
## 870 2020-03-18 North East and Yorkshire 2
## 871 2020-03-19 North East and Yorkshire 6
## 872 2020-03-20 North East and Yorkshire 5
## 873 2020-03-21 North East and Yorkshire 6
## 874 2020-03-22 North East and Yorkshire 7
## 875 2020-03-23 North East and Yorkshire 9
## 876 2020-03-24 North East and Yorkshire 8
## 877 2020-03-25 North East and Yorkshire 18
## 878 2020-03-26 North East and Yorkshire 21
## 879 2020-03-27 North East and Yorkshire 28
## 880 2020-03-28 North East and Yorkshire 35
## 881 2020-03-29 North East and Yorkshire 38
## 882 2020-03-30 North East and Yorkshire 64
## 883 2020-03-31 North East and Yorkshire 60
## 884 2020-04-01 North East and Yorkshire 67
## 885 2020-04-02 North East and Yorkshire 75
## 886 2020-04-03 North East and Yorkshire 100
## 887 2020-04-04 North East and Yorkshire 105
## 888 2020-04-05 North East and Yorkshire 92
## 889 2020-04-06 North East and Yorkshire 96
## 890 2020-04-07 North East and Yorkshire 102
## 891 2020-04-08 North East and Yorkshire 107
## 892 2020-04-09 North East and Yorkshire 111
## 893 2020-04-10 North East and Yorkshire 117
## 894 2020-04-11 North East and Yorkshire 98
## 895 2020-04-12 North East and Yorkshire 84
## 896 2020-04-13 North East and Yorkshire 94
## 897 2020-04-14 North East and Yorkshire 107
## 898 2020-04-15 North East and Yorkshire 96
## 899 2020-04-16 North East and Yorkshire 103
## 900 2020-04-17 North East and Yorkshire 88
## 901 2020-04-18 North East and Yorkshire 95
## 902 2020-04-19 North East and Yorkshire 88
## 903 2020-04-20 North East and Yorkshire 100
## 904 2020-04-21 North East and Yorkshire 76
## 905 2020-04-22 North East and Yorkshire 84
## 906 2020-04-23 North East and Yorkshire 63
## 907 2020-04-24 North East and Yorkshire 72
## 908 2020-04-25 North East and Yorkshire 69
## 909 2020-04-26 North East and Yorkshire 65
## 910 2020-04-27 North East and Yorkshire 65
## 911 2020-04-28 North East and Yorkshire 57
## 912 2020-04-29 North East and Yorkshire 69
## 913 2020-04-30 North East and Yorkshire 57
## 914 2020-05-01 North East and Yorkshire 64
## 915 2020-05-02 North East and Yorkshire 48
## 916 2020-05-03 North East and Yorkshire 40
## 917 2020-05-04 North East and Yorkshire 49
## 918 2020-05-05 North East and Yorkshire 40
## 919 2020-05-06 North East and Yorkshire 51
## 920 2020-05-07 North East and Yorkshire 45
## 921 2020-05-08 North East and Yorkshire 42
## 922 2020-05-09 North East and Yorkshire 44
## 923 2020-05-10 North East and Yorkshire 40
## 924 2020-05-11 North East and Yorkshire 29
## 925 2020-05-12 North East and Yorkshire 27
## 926 2020-05-13 North East and Yorkshire 28
## 927 2020-05-14 North East and Yorkshire 31
## 928 2020-05-15 North East and Yorkshire 32
## 929 2020-05-16 North East and Yorkshire 35
## 930 2020-05-17 North East and Yorkshire 26
## 931 2020-05-18 North East and Yorkshire 30
## 932 2020-05-19 North East and Yorkshire 27
## 933 2020-05-20 North East and Yorkshire 22
## 934 2020-05-21 North East and Yorkshire 33
## 935 2020-05-22 North East and Yorkshire 22
## 936 2020-05-23 North East and Yorkshire 18
## 937 2020-05-24 North East and Yorkshire 26
## 938 2020-05-25 North East and Yorkshire 21
## 939 2020-05-26 North East and Yorkshire 21
## 940 2020-05-27 North East and Yorkshire 22
## 941 2020-05-28 North East and Yorkshire 21
## 942 2020-05-29 North East and Yorkshire 25
## 943 2020-05-30 North East and Yorkshire 20
## 944 2020-05-31 North East and Yorkshire 20
## 945 2020-06-01 North East and Yorkshire 17
## 946 2020-06-02 North East and Yorkshire 23
## 947 2020-06-03 North East and Yorkshire 24
## 948 2020-06-04 North East and Yorkshire 17
## 949 2020-06-05 North East and Yorkshire 18
## 950 2020-06-06 North East and Yorkshire 21
## 951 2020-06-07 North East and Yorkshire 14
## 952 2020-06-08 North East and Yorkshire 11
## 953 2020-06-09 North East and Yorkshire 12
## 954 2020-06-10 North East and Yorkshire 19
## 955 2020-06-11 North East and Yorkshire 7
## 956 2020-06-12 North East and Yorkshire 9
## 957 2020-06-13 North East and Yorkshire 10
## 958 2020-06-14 North East and Yorkshire 11
## 959 2020-06-15 North East and Yorkshire 9
## 960 2020-06-16 North East and Yorkshire 10
## 961 2020-06-17 North East and Yorkshire 9
## 962 2020-06-18 North East and Yorkshire 11
## 963 2020-06-19 North East and Yorkshire 6
## 964 2020-06-20 North East and Yorkshire 5
## 965 2020-06-21 North East and Yorkshire 4
## 966 2020-06-22 North East and Yorkshire 7
## 967 2020-06-23 North East and Yorkshire 8
## 968 2020-06-24 North East and Yorkshire 10
## 969 2020-06-25 North East and Yorkshire 4
## 970 2020-06-26 North East and Yorkshire 8
## 971 2020-06-27 North East and Yorkshire 4
## 972 2020-06-28 North East and Yorkshire 5
## 973 2020-06-29 North East and Yorkshire 2
## 974 2020-06-30 North East and Yorkshire 7
## 975 2020-07-01 North East and Yorkshire 1
## 976 2020-07-02 North East and Yorkshire 4
## 977 2020-07-03 North East and Yorkshire 4
## 978 2020-07-04 North East and Yorkshire 4
## 979 2020-07-05 North East and Yorkshire 3
## 980 2020-07-06 North East and Yorkshire 2
## 981 2020-07-07 North East and Yorkshire 3
## 982 2020-07-08 North East and Yorkshire 3
## 983 2020-07-09 North East and Yorkshire 0
## 984 2020-07-10 North East and Yorkshire 3
## 985 2020-07-11 North East and Yorkshire 1
## 986 2020-07-12 North East and Yorkshire 4
## 987 2020-07-13 North East and Yorkshire 1
## 988 2020-07-14 North East and Yorkshire 1
## 989 2020-07-15 North East and Yorkshire 2
## 990 2020-07-16 North East and Yorkshire 3
## 991 2020-07-17 North East and Yorkshire 1
## 992 2020-07-18 North East and Yorkshire 2
## 993 2020-07-19 North East and Yorkshire 2
## 994 2020-07-20 North East and Yorkshire 1
## 995 2020-07-21 North East and Yorkshire 1
## 996 2020-07-22 North East and Yorkshire 6
## 997 2020-07-23 North East and Yorkshire 0
## 998 2020-07-24 North East and Yorkshire 1
## 999 2020-07-25 North East and Yorkshire 5
## 1000 2020-07-26 North East and Yorkshire 1
## 1001 2020-07-27 North East and Yorkshire 0
## 1002 2020-07-28 North East and Yorkshire 2
## 1003 2020-07-29 North East and Yorkshire 1
## 1004 2020-07-30 North East and Yorkshire 0
## 1005 2020-07-31 North East and Yorkshire 1
## 1006 2020-08-01 North East and Yorkshire 3
## 1007 2020-08-02 North East and Yorkshire 2
## 1008 2020-08-03 North East and Yorkshire 1
## 1009 2020-08-04 North East and Yorkshire 2
## 1010 2020-08-05 North East and Yorkshire 1
## 1011 2020-08-06 North East and Yorkshire 4
## 1012 2020-08-07 North East and Yorkshire 0
## 1013 2020-08-08 North East and Yorkshire 2
## 1014 2020-08-09 North East and Yorkshire 3
## 1015 2020-08-10 North East and Yorkshire 3
## 1016 2020-08-11 North East and Yorkshire 2
## 1017 2020-08-12 North East and Yorkshire 2
## 1018 2020-08-13 North East and Yorkshire 0
## 1019 2020-08-14 North East and Yorkshire 1
## 1020 2020-08-15 North East and Yorkshire 1
## 1021 2020-08-16 North East and Yorkshire 0
## 1022 2020-08-17 North East and Yorkshire 6
## 1023 2020-08-18 North East and Yorkshire 1
## 1024 2020-08-19 North East and Yorkshire 0
## 1025 2020-08-20 North East and Yorkshire 0
## 1026 2020-08-21 North East and Yorkshire 1
## 1027 2020-08-22 North East and Yorkshire 1
## 1028 2020-08-23 North East and Yorkshire 3
## 1029 2020-08-24 North East and Yorkshire 0
## 1030 2020-08-25 North East and Yorkshire 1
## 1031 2020-08-26 North East and Yorkshire 2
## 1032 2020-08-27 North East and Yorkshire 1
## 1033 2020-08-28 North East and Yorkshire 0
## 1034 2020-08-29 North East and Yorkshire 1
## 1035 2020-08-30 North East and Yorkshire 0
## 1036 2020-08-31 North East and Yorkshire 0
## 1037 2020-09-01 North East and Yorkshire 2
## 1038 2020-09-02 North East and Yorkshire 3
## 1039 2020-09-03 North East and Yorkshire 1
## 1040 2020-09-04 North East and Yorkshire 1
## 1041 2020-09-05 North East and Yorkshire 2
## 1042 2020-09-06 North East and Yorkshire 1
## 1043 2020-09-07 North East and Yorkshire 0
## 1044 2020-09-08 North East and Yorkshire 1
## 1045 2020-09-09 North East and Yorkshire 2
## 1046 2020-09-10 North East and Yorkshire 0
## 1047 2020-09-11 North East and Yorkshire 3
## 1048 2020-09-12 North East and Yorkshire 1
## 1049 2020-09-13 North East and Yorkshire 3
## 1050 2020-09-14 North East and Yorkshire 4
## 1051 2020-09-15 North East and Yorkshire 3
## 1052 2020-09-16 North East and Yorkshire 3
## 1053 2020-09-17 North East and Yorkshire 5
## 1054 2020-09-18 North East and Yorkshire 6
## 1055 2020-09-19 North East and Yorkshire 2
## 1056 2020-09-20 North East and Yorkshire 9
## 1057 2020-09-21 North East and Yorkshire 7
## 1058 2020-09-22 North East and Yorkshire 5
## 1059 2020-09-23 North East and Yorkshire 6
## 1060 2020-09-24 North East and Yorkshire 3
## 1061 2020-09-25 North East and Yorkshire 5
## 1062 2020-09-26 North East and Yorkshire 7
## 1063 2020-09-27 North East and Yorkshire 10
## 1064 2020-09-28 North East and Yorkshire 6
## 1065 2020-09-29 North East and Yorkshire 7
## 1066 2020-09-30 North East and Yorkshire 7
## 1067 2020-10-01 North East and Yorkshire 8
## 1068 2020-10-02 North East and Yorkshire 16
## 1069 2020-10-03 North East and Yorkshire 12
## 1070 2020-10-04 North East and Yorkshire 13
## 1071 2020-10-05 North East and Yorkshire 10
## 1072 2020-10-06 North East and Yorkshire 15
## 1073 2020-10-07 North East and Yorkshire 13
## 1074 2020-10-08 North East and Yorkshire 16
## 1075 2020-10-09 North East and Yorkshire 10
## 1076 2020-10-10 North East and Yorkshire 16
## 1077 2020-10-11 North East and Yorkshire 16
## 1078 2020-10-12 North East and Yorkshire 15
## 1079 2020-10-13 North East and Yorkshire 21
## 1080 2020-10-14 North East and Yorkshire 20
## 1081 2020-10-15 North East and Yorkshire 23
## 1082 2020-10-16 North East and Yorkshire 24
## 1083 2020-10-17 North East and Yorkshire 34
## 1084 2020-10-18 North East and Yorkshire 22
## 1085 2020-10-19 North East and Yorkshire 34
## 1086 2020-10-20 North East and Yorkshire 36
## 1087 2020-10-21 North East and Yorkshire 42
## 1088 2020-10-22 North East and Yorkshire 33
## 1089 2020-10-23 North East and Yorkshire 31
## 1090 2020-10-24 North East and Yorkshire 34
## 1091 2020-10-25 North East and Yorkshire 35
## 1092 2020-10-26 North East and Yorkshire 44
## 1093 2020-10-27 North East and Yorkshire 45
## 1094 2020-10-28 North East and Yorkshire 38
## 1095 2020-10-29 North East and Yorkshire 51
## 1096 2020-10-30 North East and Yorkshire 48
## 1097 2020-10-31 North East and Yorkshire 57
## 1098 2020-11-01 North East and Yorkshire 46
## 1099 2020-11-02 North East and Yorkshire 49
## 1100 2020-11-03 North East and Yorkshire 48
## 1101 2020-11-04 North East and Yorkshire 56
## 1102 2020-11-05 North East and Yorkshire 56
## 1103 2020-11-06 North East and Yorkshire 56
## 1104 2020-11-07 North East and Yorkshire 75
## 1105 2020-11-08 North East and Yorkshire 60
## 1106 2020-11-09 North East and Yorkshire 87
## 1107 2020-11-10 North East and Yorkshire 63
## 1108 2020-11-11 North East and Yorkshire 59
## 1109 2020-11-12 North East and Yorkshire 76
## 1110 2020-11-13 North East and Yorkshire 78
## 1111 2020-11-14 North East and Yorkshire 72
## 1112 2020-11-15 North East and Yorkshire 75
## 1113 2020-11-16 North East and Yorkshire 50
## 1114 2020-11-17 North East and Yorkshire 68
## 1115 2020-11-18 North East and Yorkshire 79
## 1116 2020-11-19 North East and Yorkshire 71
## 1117 2020-11-20 North East and Yorkshire 75
## 1118 2020-11-21 North East and Yorkshire 54
## 1119 2020-11-22 North East and Yorkshire 80
## 1120 2020-11-23 North East and Yorkshire 80
## 1121 2020-11-24 North East and Yorkshire 77
## 1122 2020-11-25 North East and Yorkshire 63
## 1123 2020-11-26 North East and Yorkshire 62
## 1124 2020-11-27 North East and Yorkshire 56
## 1125 2020-11-28 North East and Yorkshire 71
## 1126 2020-11-29 North East and Yorkshire 59
## 1127 2020-11-30 North East and Yorkshire 50
## 1128 2020-12-01 North East and Yorkshire 42
## 1129 2020-12-02 North East and Yorkshire 53
## 1130 2020-12-03 North East and Yorkshire 67
## 1131 2020-12-04 North East and Yorkshire 58
## 1132 2020-12-05 North East and Yorkshire 44
## 1133 2020-12-06 North East and Yorkshire 59
## 1134 2020-12-07 North East and Yorkshire 48
## 1135 2020-12-08 North East and Yorkshire 40
## 1136 2020-12-09 North East and Yorkshire 6
## 1137 2020-03-01 North West 0
## 1138 2020-03-02 North West 0
## 1139 2020-03-03 North West 0
## 1140 2020-03-04 North West 0
## 1141 2020-03-05 North West 1
## 1142 2020-03-06 North West 0
## 1143 2020-03-07 North West 0
## 1144 2020-03-08 North West 1
## 1145 2020-03-09 North West 0
## 1146 2020-03-10 North West 0
## 1147 2020-03-11 North West 0
## 1148 2020-03-12 North West 2
## 1149 2020-03-13 North West 3
## 1150 2020-03-14 North West 1
## 1151 2020-03-15 North West 4
## 1152 2020-03-16 North West 2
## 1153 2020-03-17 North West 4
## 1154 2020-03-18 North West 6
## 1155 2020-03-19 North West 7
## 1156 2020-03-20 North West 10
## 1157 2020-03-21 North West 11
## 1158 2020-03-22 North West 13
## 1159 2020-03-23 North West 15
## 1160 2020-03-24 North West 21
## 1161 2020-03-25 North West 21
## 1162 2020-03-26 North West 29
## 1163 2020-03-27 North West 36
## 1164 2020-03-28 North West 28
## 1165 2020-03-29 North West 46
## 1166 2020-03-30 North West 67
## 1167 2020-03-31 North West 52
## 1168 2020-04-01 North West 86
## 1169 2020-04-02 North West 96
## 1170 2020-04-03 North West 95
## 1171 2020-04-04 North West 98
## 1172 2020-04-05 North West 102
## 1173 2020-04-06 North West 100
## 1174 2020-04-07 North West 136
## 1175 2020-04-08 North West 127
## 1176 2020-04-09 North West 119
## 1177 2020-04-10 North West 117
## 1178 2020-04-11 North West 138
## 1179 2020-04-12 North West 125
## 1180 2020-04-13 North West 130
## 1181 2020-04-14 North West 130
## 1182 2020-04-15 North West 114
## 1183 2020-04-16 North West 135
## 1184 2020-04-17 North West 98
## 1185 2020-04-18 North West 113
## 1186 2020-04-19 North West 71
## 1187 2020-04-20 North West 83
## 1188 2020-04-21 North West 76
## 1189 2020-04-22 North West 86
## 1190 2020-04-23 North West 85
## 1191 2020-04-24 North West 66
## 1192 2020-04-25 North West 66
## 1193 2020-04-26 North West 55
## 1194 2020-04-27 North West 54
## 1195 2020-04-28 North West 57
## 1196 2020-04-29 North West 63
## 1197 2020-04-30 North West 60
## 1198 2020-05-01 North West 45
## 1199 2020-05-02 North West 56
## 1200 2020-05-03 North West 55
## 1201 2020-05-04 North West 48
## 1202 2020-05-05 North West 48
## 1203 2020-05-06 North West 44
## 1204 2020-05-07 North West 49
## 1205 2020-05-08 North West 42
## 1206 2020-05-09 North West 31
## 1207 2020-05-10 North West 42
## 1208 2020-05-11 North West 35
## 1209 2020-05-12 North West 38
## 1210 2020-05-13 North West 25
## 1211 2020-05-14 North West 26
## 1212 2020-05-15 North West 33
## 1213 2020-05-16 North West 32
## 1214 2020-05-17 North West 24
## 1215 2020-05-18 North West 31
## 1216 2020-05-19 North West 35
## 1217 2020-05-20 North West 27
## 1218 2020-05-21 North West 28
## 1219 2020-05-22 North West 26
## 1220 2020-05-23 North West 31
## 1221 2020-05-24 North West 26
## 1222 2020-05-25 North West 31
## 1223 2020-05-26 North West 27
## 1224 2020-05-27 North West 27
## 1225 2020-05-28 North West 28
## 1226 2020-05-29 North West 20
## 1227 2020-05-30 North West 19
## 1228 2020-05-31 North West 13
## 1229 2020-06-01 North West 12
## 1230 2020-06-02 North West 27
## 1231 2020-06-03 North West 22
## 1232 2020-06-04 North West 22
## 1233 2020-06-05 North West 16
## 1234 2020-06-06 North West 26
## 1235 2020-06-07 North West 20
## 1236 2020-06-08 North West 23
## 1237 2020-06-09 North West 17
## 1238 2020-06-10 North West 16
## 1239 2020-06-11 North West 16
## 1240 2020-06-12 North West 11
## 1241 2020-06-13 North West 10
## 1242 2020-06-14 North West 15
## 1243 2020-06-15 North West 16
## 1244 2020-06-16 North West 16
## 1245 2020-06-17 North West 13
## 1246 2020-06-18 North West 14
## 1247 2020-06-19 North West 7
## 1248 2020-06-20 North West 11
## 1249 2020-06-21 North West 8
## 1250 2020-06-22 North West 11
## 1251 2020-06-23 North West 13
## 1252 2020-06-24 North West 13
## 1253 2020-06-25 North West 15
## 1254 2020-06-26 North West 6
## 1255 2020-06-27 North West 7
## 1256 2020-06-28 North West 9
## 1257 2020-06-29 North West 9
## 1258 2020-06-30 North West 7
## 1259 2020-07-01 North West 3
## 1260 2020-07-02 North West 6
## 1261 2020-07-03 North West 7
## 1262 2020-07-04 North West 4
## 1263 2020-07-05 North West 6
## 1264 2020-07-06 North West 9
## 1265 2020-07-07 North West 8
## 1266 2020-07-08 North West 5
## 1267 2020-07-09 North West 10
## 1268 2020-07-10 North West 2
## 1269 2020-07-11 North West 5
## 1270 2020-07-12 North West 0
## 1271 2020-07-13 North West 6
## 1272 2020-07-14 North West 4
## 1273 2020-07-15 North West 5
## 1274 2020-07-16 North West 2
## 1275 2020-07-17 North West 4
## 1276 2020-07-18 North West 5
## 1277 2020-07-19 North West 3
## 1278 2020-07-20 North West 0
## 1279 2020-07-21 North West 2
## 1280 2020-07-22 North West 3
## 1281 2020-07-23 North West 3
## 1282 2020-07-24 North West 1
## 1283 2020-07-25 North West 1
## 1284 2020-07-26 North West 3
## 1285 2020-07-27 North West 1
## 1286 2020-07-28 North West 1
## 1287 2020-07-29 North West 2
## 1288 2020-07-30 North West 2
## 1289 2020-07-31 North West 0
## 1290 2020-08-01 North West 2
## 1291 2020-08-02 North West 1
## 1292 2020-08-03 North West 8
## 1293 2020-08-04 North West 3
## 1294 2020-08-05 North West 2
## 1295 2020-08-06 North West 2
## 1296 2020-08-07 North West 2
## 1297 2020-08-08 North West 2
## 1298 2020-08-09 North West 3
## 1299 2020-08-10 North West 2
## 1300 2020-08-11 North West 3
## 1301 2020-08-12 North West 0
## 1302 2020-08-13 North West 2
## 1303 2020-08-14 North West 2
## 1304 2020-08-15 North West 6
## 1305 2020-08-16 North West 2
## 1306 2020-08-17 North West 1
## 1307 2020-08-18 North West 2
## 1308 2020-08-19 North West 1
## 1309 2020-08-20 North West 1
## 1310 2020-08-21 North West 4
## 1311 2020-08-22 North West 3
## 1312 2020-08-23 North West 5
## 1313 2020-08-24 North West 4
## 1314 2020-08-25 North West 3
## 1315 2020-08-26 North West 4
## 1316 2020-08-27 North West 1
## 1317 2020-08-28 North West 2
## 1318 2020-08-29 North West 0
## 1319 2020-08-30 North West 2
## 1320 2020-08-31 North West 3
## 1321 2020-09-01 North West 0
## 1322 2020-09-02 North West 2
## 1323 2020-09-03 North West 1
## 1324 2020-09-04 North West 3
## 1325 2020-09-05 North West 6
## 1326 2020-09-06 North West 1
## 1327 2020-09-07 North West 8
## 1328 2020-09-08 North West 6
## 1329 2020-09-09 North West 5
## 1330 2020-09-10 North West 5
## 1331 2020-09-11 North West 1
## 1332 2020-09-12 North West 4
## 1333 2020-09-13 North West 2
## 1334 2020-09-14 North West 4
## 1335 2020-09-15 North West 4
## 1336 2020-09-16 North West 6
## 1337 2020-09-17 North West 7
## 1338 2020-09-18 North West 6
## 1339 2020-09-19 North West 3
## 1340 2020-09-20 North West 2
## 1341 2020-09-21 North West 2
## 1342 2020-09-22 North West 9
## 1343 2020-09-23 North West 14
## 1344 2020-09-24 North West 10
## 1345 2020-09-25 North West 8
## 1346 2020-09-26 North West 14
## 1347 2020-09-27 North West 11
## 1348 2020-09-28 North West 15
## 1349 2020-09-29 North West 12
## 1350 2020-09-30 North West 17
## 1351 2020-10-01 North West 17
## 1352 2020-10-02 North West 20
## 1353 2020-10-03 North West 15
## 1354 2020-10-04 North West 15
## 1355 2020-10-05 North West 15
## 1356 2020-10-06 North West 20
## 1357 2020-10-07 North West 20
## 1358 2020-10-08 North West 22
## 1359 2020-10-09 North West 23
## 1360 2020-10-10 North West 31
## 1361 2020-10-11 North West 31
## 1362 2020-10-12 North West 35
## 1363 2020-10-13 North West 26
## 1364 2020-10-14 North West 35
## 1365 2020-10-15 North West 36
## 1366 2020-10-16 North West 34
## 1367 2020-10-17 North West 52
## 1368 2020-10-18 North West 40
## 1369 2020-10-19 North West 43
## 1370 2020-10-20 North West 48
## 1371 2020-10-21 North West 51
## 1372 2020-10-22 North West 49
## 1373 2020-10-23 North West 50
## 1374 2020-10-24 North West 51
## 1375 2020-10-25 North West 63
## 1376 2020-10-26 North West 53
## 1377 2020-10-27 North West 49
## 1378 2020-10-28 North West 57
## 1379 2020-10-29 North West 74
## 1380 2020-10-30 North West 73
## 1381 2020-10-31 North West 63
## 1382 2020-11-01 North West 76
## 1383 2020-11-02 North West 65
## 1384 2020-11-03 North West 76
## 1385 2020-11-04 North West 64
## 1386 2020-11-05 North West 67
## 1387 2020-11-06 North West 74
## 1388 2020-11-07 North West 75
## 1389 2020-11-08 North West 83
## 1390 2020-11-09 North West 81
## 1391 2020-11-10 North West 68
## 1392 2020-11-11 North West 60
## 1393 2020-11-12 North West 64
## 1394 2020-11-13 North West 79
## 1395 2020-11-14 North West 61
## 1396 2020-11-15 North West 75
## 1397 2020-11-16 North West 74
## 1398 2020-11-17 North West 73
## 1399 2020-11-18 North West 70
## 1400 2020-11-19 North West 67
## 1401 2020-11-20 North West 52
## 1402 2020-11-21 North West 67
## 1403 2020-11-22 North West 51
## 1404 2020-11-23 North West 54
## 1405 2020-11-24 North West 64
## 1406 2020-11-25 North West 65
## 1407 2020-11-26 North West 52
## 1408 2020-11-27 North West 49
## 1409 2020-11-28 North West 46
## 1410 2020-11-29 North West 53
## 1411 2020-11-30 North West 46
## 1412 2020-12-01 North West 52
## 1413 2020-12-02 North West 44
## 1414 2020-12-03 North West 42
## 1415 2020-12-04 North West 43
## 1416 2020-12-05 North West 35
## 1417 2020-12-06 North West 40
## 1418 2020-12-07 North West 47
## 1419 2020-12-08 North West 26
## 1420 2020-12-09 North West 11
## 1421 2020-03-01 South East 0
## 1422 2020-03-02 South East 0
## 1423 2020-03-03 South East 1
## 1424 2020-03-04 South East 0
## 1425 2020-03-05 South East 1
## 1426 2020-03-06 South East 0
## 1427 2020-03-07 South East 0
## 1428 2020-03-08 South East 1
## 1429 2020-03-09 South East 1
## 1430 2020-03-10 South East 1
## 1431 2020-03-11 South East 1
## 1432 2020-03-12 South East 0
## 1433 2020-03-13 South East 1
## 1434 2020-03-14 South East 1
## 1435 2020-03-15 South East 5
## 1436 2020-03-16 South East 8
## 1437 2020-03-17 South East 7
## 1438 2020-03-18 South East 10
## 1439 2020-03-19 South East 9
## 1440 2020-03-20 South East 13
## 1441 2020-03-21 South East 7
## 1442 2020-03-22 South East 25
## 1443 2020-03-23 South East 20
## 1444 2020-03-24 South East 22
## 1445 2020-03-25 South East 29
## 1446 2020-03-26 South East 35
## 1447 2020-03-27 South East 36
## 1448 2020-03-28 South East 36
## 1449 2020-03-29 South East 55
## 1450 2020-03-30 South East 58
## 1451 2020-03-31 South East 65
## 1452 2020-04-01 South East 66
## 1453 2020-04-02 South East 55
## 1454 2020-04-03 South East 72
## 1455 2020-04-04 South East 80
## 1456 2020-04-05 South East 82
## 1457 2020-04-06 South East 88
## 1458 2020-04-07 South East 100
## 1459 2020-04-08 South East 83
## 1460 2020-04-09 South East 104
## 1461 2020-04-10 South East 88
## 1462 2020-04-11 South East 88
## 1463 2020-04-12 South East 88
## 1464 2020-04-13 South East 84
## 1465 2020-04-14 South East 65
## 1466 2020-04-15 South East 72
## 1467 2020-04-16 South East 56
## 1468 2020-04-17 South East 86
## 1469 2020-04-18 South East 57
## 1470 2020-04-19 South East 70
## 1471 2020-04-20 South East 87
## 1472 2020-04-21 South East 51
## 1473 2020-04-22 South East 54
## 1474 2020-04-23 South East 57
## 1475 2020-04-24 South East 64
## 1476 2020-04-25 South East 51
## 1477 2020-04-26 South East 51
## 1478 2020-04-27 South East 41
## 1479 2020-04-28 South East 40
## 1480 2020-04-29 South East 47
## 1481 2020-04-30 South East 29
## 1482 2020-05-01 South East 37
## 1483 2020-05-02 South East 36
## 1484 2020-05-03 South East 17
## 1485 2020-05-04 South East 35
## 1486 2020-05-05 South East 29
## 1487 2020-05-06 South East 25
## 1488 2020-05-07 South East 27
## 1489 2020-05-08 South East 26
## 1490 2020-05-09 South East 28
## 1491 2020-05-10 South East 19
## 1492 2020-05-11 South East 25
## 1493 2020-05-12 South East 27
## 1494 2020-05-13 South East 18
## 1495 2020-05-14 South East 32
## 1496 2020-05-15 South East 25
## 1497 2020-05-16 South East 22
## 1498 2020-05-17 South East 18
## 1499 2020-05-18 South East 22
## 1500 2020-05-19 South East 12
## 1501 2020-05-20 South East 22
## 1502 2020-05-21 South East 15
## 1503 2020-05-22 South East 17
## 1504 2020-05-23 South East 21
## 1505 2020-05-24 South East 17
## 1506 2020-05-25 South East 13
## 1507 2020-05-26 South East 19
## 1508 2020-05-27 South East 19
## 1509 2020-05-28 South East 12
## 1510 2020-05-29 South East 22
## 1511 2020-05-30 South East 8
## 1512 2020-05-31 South East 12
## 1513 2020-06-01 South East 11
## 1514 2020-06-02 South East 13
## 1515 2020-06-03 South East 18
## 1516 2020-06-04 South East 11
## 1517 2020-06-05 South East 11
## 1518 2020-06-06 South East 10
## 1519 2020-06-07 South East 12
## 1520 2020-06-08 South East 8
## 1521 2020-06-09 South East 10
## 1522 2020-06-10 South East 11
## 1523 2020-06-11 South East 5
## 1524 2020-06-12 South East 6
## 1525 2020-06-13 South East 7
## 1526 2020-06-14 South East 7
## 1527 2020-06-15 South East 8
## 1528 2020-06-16 South East 14
## 1529 2020-06-17 South East 9
## 1530 2020-06-18 South East 4
## 1531 2020-06-19 South East 7
## 1532 2020-06-20 South East 5
## 1533 2020-06-21 South East 3
## 1534 2020-06-22 South East 2
## 1535 2020-06-23 South East 9
## 1536 2020-06-24 South East 7
## 1537 2020-06-25 South East 5
## 1538 2020-06-26 South East 8
## 1539 2020-06-27 South East 9
## 1540 2020-06-28 South East 6
## 1541 2020-06-29 South East 5
## 1542 2020-06-30 South East 5
## 1543 2020-07-01 South East 2
## 1544 2020-07-02 South East 8
## 1545 2020-07-03 South East 3
## 1546 2020-07-04 South East 6
## 1547 2020-07-05 South East 5
## 1548 2020-07-06 South East 4
## 1549 2020-07-07 South East 6
## 1550 2020-07-08 South East 3
## 1551 2020-07-09 South East 7
## 1552 2020-07-10 South East 3
## 1553 2020-07-11 South East 4
## 1554 2020-07-12 South East 5
## 1555 2020-07-13 South East 5
## 1556 2020-07-14 South East 5
## 1557 2020-07-15 South East 6
## 1558 2020-07-16 South East 3
## 1559 2020-07-17 South East 1
## 1560 2020-07-18 South East 5
## 1561 2020-07-19 South East 2
## 1562 2020-07-20 South East 6
## 1563 2020-07-21 South East 4
## 1564 2020-07-22 South East 2
## 1565 2020-07-23 South East 3
## 1566 2020-07-24 South East 1
## 1567 2020-07-25 South East 1
## 1568 2020-07-26 South East 3
## 1569 2020-07-27 South East 1
## 1570 2020-07-28 South East 3
## 1571 2020-07-29 South East 2
## 1572 2020-07-30 South East 3
## 1573 2020-07-31 South East 1
## 1574 2020-08-01 South East 2
## 1575 2020-08-02 South East 4
## 1576 2020-08-03 South East 0
## 1577 2020-08-04 South East 0
## 1578 2020-08-05 South East 0
## 1579 2020-08-06 South East 2
## 1580 2020-08-07 South East 0
## 1581 2020-08-08 South East 2
## 1582 2020-08-09 South East 0
## 1583 2020-08-10 South East 2
## 1584 2020-08-11 South East 1
## 1585 2020-08-12 South East 1
## 1586 2020-08-13 South East 0
## 1587 2020-08-14 South East 0
## 1588 2020-08-15 South East 2
## 1589 2020-08-16 South East 1
## 1590 2020-08-17 South East 0
## 1591 2020-08-18 South East 2
## 1592 2020-08-19 South East 1
## 1593 2020-08-20 South East 0
## 1594 2020-08-21 South East 0
## 1595 2020-08-22 South East 0
## 1596 2020-08-23 South East 1
## 1597 2020-08-24 South East 0
## 1598 2020-08-25 South East 1
## 1599 2020-08-26 South East 0
## 1600 2020-08-27 South East 1
## 1601 2020-08-28 South East 2
## 1602 2020-08-29 South East 1
## 1603 2020-08-30 South East 0
## 1604 2020-08-31 South East 2
## 1605 2020-09-01 South East 1
## 1606 2020-09-02 South East 1
## 1607 2020-09-03 South East 0
## 1608 2020-09-04 South East 1
## 1609 2020-09-05 South East 0
## 1610 2020-09-06 South East 1
## 1611 2020-09-07 South East 0
## 1612 2020-09-08 South East 0
## 1613 2020-09-09 South East 0
## 1614 2020-09-10 South East 1
## 1615 2020-09-11 South East 1
## 1616 2020-09-12 South East 0
## 1617 2020-09-13 South East 3
## 1618 2020-09-14 South East 1
## 1619 2020-09-15 South East 2
## 1620 2020-09-16 South East 2
## 1621 2020-09-17 South East 3
## 1622 2020-09-18 South East 1
## 1623 2020-09-19 South East 1
## 1624 2020-09-20 South East 0
## 1625 2020-09-21 South East 3
## 1626 2020-09-22 South East 0
## 1627 2020-09-23 South East 2
## 1628 2020-09-24 South East 1
## 1629 2020-09-25 South East 3
## 1630 2020-09-26 South East 2
## 1631 2020-09-27 South East 2
## 1632 2020-09-28 South East 6
## 1633 2020-09-29 South East 3
## 1634 2020-09-30 South East 4
## 1635 2020-10-01 South East 4
## 1636 2020-10-02 South East 2
## 1637 2020-10-03 South East 1
## 1638 2020-10-04 South East 1
## 1639 2020-10-05 South East 2
## 1640 2020-10-06 South East 1
## 1641 2020-10-07 South East 4
## 1642 2020-10-08 South East 1
## 1643 2020-10-09 South East 1
## 1644 2020-10-10 South East 3
## 1645 2020-10-11 South East 3
## 1646 2020-10-12 South East 4
## 1647 2020-10-13 South East 2
## 1648 2020-10-14 South East 2
## 1649 2020-10-15 South East 3
## 1650 2020-10-16 South East 2
## 1651 2020-10-17 South East 3
## 1652 2020-10-18 South East 4
## 1653 2020-10-19 South East 7
## 1654 2020-10-20 South East 8
## 1655 2020-10-21 South East 9
## 1656 2020-10-22 South East 5
## 1657 2020-10-23 South East 7
## 1658 2020-10-24 South East 5
## 1659 2020-10-25 South East 9
## 1660 2020-10-26 South East 13
## 1661 2020-10-27 South East 10
## 1662 2020-10-28 South East 10
## 1663 2020-10-29 South East 7
## 1664 2020-10-30 South East 6
## 1665 2020-10-31 South East 15
## 1666 2020-11-01 South East 18
## 1667 2020-11-02 South East 13
## 1668 2020-11-03 South East 16
## 1669 2020-11-04 South East 10
## 1670 2020-11-05 South East 10
## 1671 2020-11-06 South East 16
## 1672 2020-11-07 South East 17
## 1673 2020-11-08 South East 18
## 1674 2020-11-09 South East 19
## 1675 2020-11-10 South East 20
## 1676 2020-11-11 South East 19
## 1677 2020-11-12 South East 20
## 1678 2020-11-13 South East 12
## 1679 2020-11-14 South East 24
## 1680 2020-11-15 South East 25
## 1681 2020-11-16 South East 22
## 1682 2020-11-17 South East 23
## 1683 2020-11-18 South East 26
## 1684 2020-11-19 South East 21
## 1685 2020-11-20 South East 18
## 1686 2020-11-21 South East 23
## 1687 2020-11-22 South East 29
## 1688 2020-11-23 South East 28
## 1689 2020-11-24 South East 26
## 1690 2020-11-25 South East 39
## 1691 2020-11-26 South East 30
## 1692 2020-11-27 South East 31
## 1693 2020-11-28 South East 22
## 1694 2020-11-29 South East 35
## 1695 2020-11-30 South East 21
## 1696 2020-12-01 South East 27
## 1697 2020-12-02 South East 28
## 1698 2020-12-03 South East 33
## 1699 2020-12-04 South East 34
## 1700 2020-12-05 South East 25
## 1701 2020-12-06 South East 24
## 1702 2020-12-07 South East 15
## 1703 2020-12-08 South East 27
## 1704 2020-12-09 South East 3
## 1705 2020-03-01 South West 0
## 1706 2020-03-02 South West 0
## 1707 2020-03-03 South West 0
## 1708 2020-03-04 South West 0
## 1709 2020-03-05 South West 0
## 1710 2020-03-06 South West 0
## 1711 2020-03-07 South West 0
## 1712 2020-03-08 South West 0
## 1713 2020-03-09 South West 0
## 1714 2020-03-10 South West 0
## 1715 2020-03-11 South West 1
## 1716 2020-03-12 South West 0
## 1717 2020-03-13 South West 0
## 1718 2020-03-14 South West 1
## 1719 2020-03-15 South West 0
## 1720 2020-03-16 South West 0
## 1721 2020-03-17 South West 2
## 1722 2020-03-18 South West 2
## 1723 2020-03-19 South West 4
## 1724 2020-03-20 South West 3
## 1725 2020-03-21 South West 6
## 1726 2020-03-22 South West 7
## 1727 2020-03-23 South West 8
## 1728 2020-03-24 South West 7
## 1729 2020-03-25 South West 9
## 1730 2020-03-26 South West 11
## 1731 2020-03-27 South West 13
## 1732 2020-03-28 South West 21
## 1733 2020-03-29 South West 18
## 1734 2020-03-30 South West 23
## 1735 2020-03-31 South West 23
## 1736 2020-04-01 South West 21
## 1737 2020-04-02 South West 23
## 1738 2020-04-03 South West 30
## 1739 2020-04-04 South West 42
## 1740 2020-04-05 South West 32
## 1741 2020-04-06 South West 34
## 1742 2020-04-07 South West 39
## 1743 2020-04-08 South West 47
## 1744 2020-04-09 South West 24
## 1745 2020-04-10 South West 46
## 1746 2020-04-11 South West 43
## 1747 2020-04-12 South West 23
## 1748 2020-04-13 South West 27
## 1749 2020-04-14 South West 24
## 1750 2020-04-15 South West 32
## 1751 2020-04-16 South West 29
## 1752 2020-04-17 South West 33
## 1753 2020-04-18 South West 25
## 1754 2020-04-19 South West 31
## 1755 2020-04-20 South West 26
## 1756 2020-04-21 South West 26
## 1757 2020-04-22 South West 23
## 1758 2020-04-23 South West 17
## 1759 2020-04-24 South West 19
## 1760 2020-04-25 South West 15
## 1761 2020-04-26 South West 27
## 1762 2020-04-27 South West 13
## 1763 2020-04-28 South West 17
## 1764 2020-04-29 South West 15
## 1765 2020-04-30 South West 26
## 1766 2020-05-01 South West 6
## 1767 2020-05-02 South West 7
## 1768 2020-05-03 South West 10
## 1769 2020-05-04 South West 17
## 1770 2020-05-05 South West 14
## 1771 2020-05-06 South West 19
## 1772 2020-05-07 South West 16
## 1773 2020-05-08 South West 6
## 1774 2020-05-09 South West 11
## 1775 2020-05-10 South West 5
## 1776 2020-05-11 South West 8
## 1777 2020-05-12 South West 7
## 1778 2020-05-13 South West 7
## 1779 2020-05-14 South West 6
## 1780 2020-05-15 South West 4
## 1781 2020-05-16 South West 4
## 1782 2020-05-17 South West 6
## 1783 2020-05-18 South West 4
## 1784 2020-05-19 South West 6
## 1785 2020-05-20 South West 1
## 1786 2020-05-21 South West 9
## 1787 2020-05-22 South West 7
## 1788 2020-05-23 South West 6
## 1789 2020-05-24 South West 3
## 1790 2020-05-25 South West 8
## 1791 2020-05-26 South West 11
## 1792 2020-05-27 South West 5
## 1793 2020-05-28 South West 10
## 1794 2020-05-29 South West 7
## 1795 2020-05-30 South West 3
## 1796 2020-05-31 South West 2
## 1797 2020-06-01 South West 7
## 1798 2020-06-02 South West 2
## 1799 2020-06-03 South West 7
## 1800 2020-06-04 South West 2
## 1801 2020-06-05 South West 2
## 1802 2020-06-06 South West 1
## 1803 2020-06-07 South West 3
## 1804 2020-06-08 South West 3
## 1805 2020-06-09 South West 0
## 1806 2020-06-10 South West 1
## 1807 2020-06-11 South West 2
## 1808 2020-06-12 South West 2
## 1809 2020-06-13 South West 2
## 1810 2020-06-14 South West 0
## 1811 2020-06-15 South West 2
## 1812 2020-06-16 South West 2
## 1813 2020-06-17 South West 0
## 1814 2020-06-18 South West 0
## 1815 2020-06-19 South West 0
## 1816 2020-06-20 South West 2
## 1817 2020-06-21 South West 0
## 1818 2020-06-22 South West 1
## 1819 2020-06-23 South West 1
## 1820 2020-06-24 South West 1
## 1821 2020-06-25 South West 0
## 1822 2020-06-26 South West 3
## 1823 2020-06-27 South West 0
## 1824 2020-06-28 South West 0
## 1825 2020-06-29 South West 1
## 1826 2020-06-30 South West 0
## 1827 2020-07-01 South West 0
## 1828 2020-07-02 South West 0
## 1829 2020-07-03 South West 0
## 1830 2020-07-04 South West 0
## 1831 2020-07-05 South West 1
## 1832 2020-07-06 South West 0
## 1833 2020-07-07 South West 0
## 1834 2020-07-08 South West 2
## 1835 2020-07-09 South West 0
## 1836 2020-07-10 South West 1
## 1837 2020-07-11 South West 0
## 1838 2020-07-12 South West 0
## 1839 2020-07-13 South West 1
## 1840 2020-07-14 South West 0
## 1841 2020-07-15 South West 0
## 1842 2020-07-16 South West 0
## 1843 2020-07-17 South West 1
## 1844 2020-07-18 South West 0
## 1845 2020-07-19 South West 0
## 1846 2020-07-20 South West 0
## 1847 2020-07-21 South West 0
## 1848 2020-07-22 South West 0
## 1849 2020-07-23 South West 0
## 1850 2020-07-24 South West 0
## 1851 2020-07-25 South West 0
## 1852 2020-07-26 South West 0
## 1853 2020-07-27 South West 0
## 1854 2020-07-28 South West 0
## 1855 2020-07-29 South West 0
## 1856 2020-07-30 South West 1
## 1857 2020-07-31 South West 0
## 1858 2020-08-01 South West 0
## 1859 2020-08-02 South West 0
## 1860 2020-08-03 South West 0
## 1861 2020-08-04 South West 0
## 1862 2020-08-05 South West 0
## 1863 2020-08-06 South West 0
## 1864 2020-08-07 South West 0
## 1865 2020-08-08 South West 0
## 1866 2020-08-09 South West 0
## 1867 2020-08-10 South West 0
## 1868 2020-08-11 South West 0
## 1869 2020-08-12 South West 0
## 1870 2020-08-13 South West 0
## 1871 2020-08-14 South West 1
## 1872 2020-08-15 South West 0
## 1873 2020-08-16 South West 0
## 1874 2020-08-17 South West 2
## 1875 2020-08-18 South West 0
## 1876 2020-08-19 South West 0
## 1877 2020-08-20 South West 0
## 1878 2020-08-21 South West 0
## 1879 2020-08-22 South West 0
## 1880 2020-08-23 South West 0
## 1881 2020-08-24 South West 0
## 1882 2020-08-25 South West 1
## 1883 2020-08-26 South West 0
## 1884 2020-08-27 South West 1
## 1885 2020-08-28 South West 0
## 1886 2020-08-29 South West 0
## 1887 2020-08-30 South West 0
## 1888 2020-08-31 South West 0
## 1889 2020-09-01 South West 0
## 1890 2020-09-02 South West 0
## 1891 2020-09-03 South West 0
## 1892 2020-09-04 South West 0
## 1893 2020-09-05 South West 0
## 1894 2020-09-06 South West 0
## 1895 2020-09-07 South West 0
## 1896 2020-09-08 South West 1
## 1897 2020-09-09 South West 0
## 1898 2020-09-10 South West 0
## 1899 2020-09-11 South West 0
## 1900 2020-09-12 South West 0
## 1901 2020-09-13 South West 1
## 1902 2020-09-14 South West 0
## 1903 2020-09-15 South West 0
## 1904 2020-09-16 South West 0
## 1905 2020-09-17 South West 1
## 1906 2020-09-18 South West 0
## 1907 2020-09-19 South West 0
## 1908 2020-09-20 South West 1
## 1909 2020-09-21 South West 0
## 1910 2020-09-22 South West 0
## 1911 2020-09-23 South West 0
## 1912 2020-09-24 South West 1
## 1913 2020-09-25 South West 0
## 1914 2020-09-26 South West 0
## 1915 2020-09-27 South West 0
## 1916 2020-09-28 South West 0
## 1917 2020-09-29 South West 0
## 1918 2020-09-30 South West 0
## 1919 2020-10-01 South West 0
## 1920 2020-10-02 South West 1
## 1921 2020-10-03 South West 0
## 1922 2020-10-04 South West 0
## 1923 2020-10-05 South West 0
## 1924 2020-10-06 South West 1
## 1925 2020-10-07 South West 0
## 1926 2020-10-08 South West 1
## 1927 2020-10-09 South West 1
## 1928 2020-10-10 South West 0
## 1929 2020-10-11 South West 4
## 1930 2020-10-12 South West 2
## 1931 2020-10-13 South West 0
## 1932 2020-10-14 South West 3
## 1933 2020-10-15 South West 1
## 1934 2020-10-16 South West 2
## 1935 2020-10-17 South West 8
## 1936 2020-10-18 South West 2
## 1937 2020-10-19 South West 2
## 1938 2020-10-20 South West 3
## 1939 2020-10-21 South West 6
## 1940 2020-10-22 South West 6
## 1941 2020-10-23 South West 5
## 1942 2020-10-24 South West 5
## 1943 2020-10-25 South West 5
## 1944 2020-10-26 South West 7
## 1945 2020-10-27 South West 6
## 1946 2020-10-28 South West 8
## 1947 2020-10-29 South West 11
## 1948 2020-10-30 South West 8
## 1949 2020-10-31 South West 4
## 1950 2020-11-01 South West 5
## 1951 2020-11-02 South West 11
## 1952 2020-11-03 South West 7
## 1953 2020-11-04 South West 8
## 1954 2020-11-05 South West 5
## 1955 2020-11-06 South West 11
## 1956 2020-11-07 South West 10
## 1957 2020-11-08 South West 10
## 1958 2020-11-09 South West 12
## 1959 2020-11-10 South West 6
## 1960 2020-11-11 South West 13
## 1961 2020-11-12 South West 17
## 1962 2020-11-13 South West 9
## 1963 2020-11-14 South West 8
## 1964 2020-11-15 South West 16
## 1965 2020-11-16 South West 18
## 1966 2020-11-17 South West 17
## 1967 2020-11-18 South West 26
## 1968 2020-11-19 South West 15
## 1969 2020-11-20 South West 24
## 1970 2020-11-21 South West 24
## 1971 2020-11-22 South West 21
## 1972 2020-11-23 South West 14
## 1973 2020-11-24 South West 17
## 1974 2020-11-25 South West 24
## 1975 2020-11-26 South West 16
## 1976 2020-11-27 South West 21
## 1977 2020-11-28 South West 33
## 1978 2020-11-29 South West 14
## 1979 2020-11-30 South West 21
## 1980 2020-12-01 South West 18
## 1981 2020-12-02 South West 14
## 1982 2020-12-03 South West 14
## 1983 2020-12-04 South West 18
## 1984 2020-12-05 South West 15
## 1985 2020-12-06 South West 12
## 1986 2020-12-07 South West 9
## 1987 2020-12-08 South West 13
## 1988 2020-12-09 South West 5We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Thursday 10 Dec 2020.
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_reportWe plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lagThis analysis suggests that the best lag is 16 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 16 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -19.179 -9.828 -5.152 4.970 21.158
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.328e+00 7.270e-02 59.53 <2e-16 ***
## note_lag 1.847e-05 8.957e-07 20.62 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 88.17277)
##
## Null deviance: 47490 on 229 degrees of freedom
## Residual deviance: 20041 on 228 degrees of freedom
## (16 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 75.772960 1.000018
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 65.516771 87.12973
## note_lag 1.000017 1.00002
Rsq(lag_mod)
## [1] 0.5779986
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fitThis is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom")
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_regWe save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))The following information documents the system on which the document was compiled.
This provides information on the operating system.
Sys.info()
## sysname
## "Darwin"
## release
## "19.6.0"
## version
## "Darwin Kernel Version 19.6.0: Thu Oct 29 22:56:45 PDT 2020; root:xnu-6153.141.2.2~1/RELEASE_X86_64"
## nodename
## "Mac-1607682676815.local"
## machine
## "x86_64"
## login
## "root"
## user
## "runner"
## effective_user
## "runner"This provides information on the version of R used:
This provides information on the packages used:
sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.4 ggpubr_0.4.0 lubridate_1.7.9.2
## [4] chngpt_2020.10-12 cyphr_1.1.0 DT_0.16
## [7] kableExtra_1.3.1 janitor_2.0.1 remotes_2.2.0
## [10] projections_0.5.1 earlyR_0.0.5 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.3 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.6 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
## [25] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2
## [28] tidyverse_1.3.0 here_1.0.0 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] minqa_1.2.4 colorspace_2.0-0 selectr_0.4-2 ggsignif_0.6.0
## [5] ellipsis_0.3.1 rprojroot_2.0.2 snakecase_0.11.0 fs_1.5.0
## [9] rstudioapi_0.13 farver_2.0.3 fansi_0.4.1 splines_4.0.3
## [13] knitr_1.30 jsonlite_1.7.2 nloptr_1.2.2.2 broom_0.7.2
## [17] dbplyr_2.0.0 compiler_4.0.3 httr_1.4.2 backports_1.2.1
## [21] assertthat_0.2.1 Matrix_1.2-18 cli_2.2.0 htmltools_0.5.0
## [25] tools_4.0.3 gtable_0.3.0 glue_1.4.2 Rcpp_1.0.5
## [29] carData_3.0-4 cellranger_1.1.0 vctrs_0.3.5 nlme_3.1-149
## [33] matchmaker_0.1.1 crosstalk_1.1.0.1 xfun_0.19 ps_1.5.0
## [37] openxlsx_4.2.3 lme4_1.1-26 lifecycle_0.2.0 statmod_1.4.35
## [41] rstatix_0.6.0 MASS_7.3-53 scales_1.1.1 hms_0.5.3
## [45] parallel_4.0.3 sodium_1.1 yaml_2.2.1 curl_4.3
## [49] gridExtra_2.3 stringi_1.5.3 kyotil_2020.10-12 boot_1.3-25
## [53] zip_2.1.1 rlang_0.4.9 pkgconfig_2.0.3 evaluate_0.14
## [57] lattice_0.20-41 labeling_0.4.2 htmlwidgets_1.5.3 cowplot_1.1.0
## [61] tidyselect_1.1.0 plyr_1.8.6 magrittr_2.0.1 R6_2.5.0
## [65] generics_0.1.0 DBI_1.1.0 pillar_1.4.7 haven_2.3.1
## [69] foreign_0.8-80 withr_2.3.0 mgcv_1.8-33 survival_3.2-7
## [73] abind_1.4-5 modelr_0.1.8 crayon_1.3.4 car_3.0-10
## [77] utf8_1.1.4 rmarkdown_2.5 viridis_0.5.1 grid_4.0.3
## [81] readxl_1.3.1 data.table_1.13.4 reprex_0.3.0 digest_0.6.27
## [85] webshot_0.5.2 munsell_0.5.0 viridisLite_0.3.0